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Data Analytics for Nursing Education

Many in the healthcare field get into the profession because they have a passion for care. They want to help their patients overcome health challenges and have healthy lives. How might “data” fit into that? Numbers aren’t exactly the warm-fuzzy aspect patients appreciate.

But today, data analytics has become increasingly important in healthcare. Nursing education is instrumental in preparing nurses in the field for collecting, analyzing and synthesizing data.

With this knowledge base, nurses are positioning themselves to be experts in data analytics (and thus attractive candidates for hire). Yet, they need nursing educators involved to help them understand the importance of data analytics and its broader scope for application in the healthcare industry. Educators benefit from including data analytics knowledge in their preparatory curriculum.

How can they optimize their position as a nursing educator in this regard? It starts with understanding the overview of data science.

Five Steps of the Data Science Process

Data will never exist in a vacuum. There is a method of collaboration, even a step-by-step process, from which nursing professionals can optimize the data they encounter daily. The following is generally accepted as a standard for progressing through the data science process:

1) Formulating a question or problem to be addressed using data. This step involves identifying a problem or question that can be addressed using data. This might involve identifying a gap in patient care, a need for improved patient outcomes or a desire for improved efficiency in nursing practice.

2) Collecting relevant data. Identifying and collecting relevant data that can help answer the question or address the problem identified in the first step is crucial. Step two involves gathering patient data, clinical outcomes data or data on nursing practice.

3) Cleaning and preparing the data for analysis. When data comes in, it’s often considered “raw.” Therefore, it’s important to clean and prepare data to ensure it is accurate and can be effectively analyzed. Nurses are often tasked with removing missing data, addressing errors and ensuring the data are properly formatted for analysis.

4) Analyzing the data using statistical methods and machine learning algorithms. With data, there will almost always be patterns, relationships and insights nurses can glean. Statistical methods and machine learning algorithms answer the question or address the problem identified in step one. Nurses may implement regression analysis, clustering analysis or other statistical methods to analyze the data.

5) Communicating the results to stakeholders and applying findings to practice. When you’ve gotten this far, it only makes sense there is an “end result” or at least a conclusion that can lead to more data analysis. Those who have participated in data analysis are responsible for informing stakeholders of the outcomes, including patients, providers and other healthcare team members. They may be best served to create data visualizations, present data clearly and concisely and provide recommendations for practice changes based on the findings of the data analysis.

Additionally, this step involves applying the findings of the data analysis to nursing practice and patient care in order to improve outcomes and ensure the best possible patient care.

How Can Data Be Applied to Elicit Desirable Results?

Once data have gone through the above steps, they need to be applied to various healthcare scenarios. For example, there are takeaways healthcare providers can use to uplevel their practice. One noted study, using data analytics, looked at patient outcomes in respect to the level of empathy the healthcare provider portrayed. The results indicated physician empathy was associated with “better outcomes, greater patient safety and fewer malpractice claims.”

This is just one example, but there are many ways data analytics can be incorporated across the board. The following are three of the most common applications:  

1) Predictive analytics for readmission risk. Predictive analytics uses statistical algorithms and machine learning techniques to identify patients who have readmission risks. This can help healthcare providers take proactive steps to prevent readmissions, such as providing additional education and support to patients and their families or scheduling follow-up appointments more frequently. Predictive analytics can also help healthcare organizations identify patterns and risk factors contributing to readmissions, which can then inform the development of targeted interventions and programs to reduce readmission rates.

2) Population health management. Population health management employs data analytics to improve the health outcomes of a specific population. This might involve identifying high-risk patient populations, implementing preventative care measures or developing disease management programs to better manage chronic conditions. By analyzing data on patient populations, healthcare organizations can identify areas where improvements can be made in preventative care and chronic disease management, leading to improved patient outcomes and reduced healthcare costs.

3) Clinical decision support. Clinical decision support uses data analytics to provide healthcare providers with real-time, evidence-based information to guide clinical decision-making. This might involve providing alerts to healthcare providers when a patient is at high risk of a certain complication or recommending a treatment based on the patient’s medical history and current condition. Clinical decision support can help healthcare providers make more informed and accurate patient care decisions, which can lead to improved patient outcomes and reduced healthcare costs.

While these applications often involve the entire health system, nurses play an instrumental role in helping to compile data and analyze it via their close connections with the patient population.

Data Analytics Needs Educators Who Are Experts in the Field

There’s no singular course that can fully prepare nurse educators for the scope of data analysis. However, a comprehensive curriculum, such as that offered by the University of North Carolina Wilmington (UNCW), provides nursing educators the foundation they need to teach the upcoming generation about this vital skill set.

Specifically, the Health Care and Nursing Practice Informatics and Technology course “examines clinical information systems, healthcare technology and medical languages as informatics concepts are used and applied for investigative inquiry. The focus is on the impact of informatics and technology on healthcare systems and nursing practice.”

Students can complete UNCW’s Master of Science in Nursing (MSN) Nurse Educator online program in as few as 12 months and at a reasonable tuition rate. If you’re thinking about following a nursing education tract, UNCW offers a perfect opportunity.

Learn more about UNCW’s online MSN Nurse Educator program.

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